Why India Can Afford To Wait And Watch Before Regulating AI
AI systems have a significant limitation: Many of their inner workings are impenetrable, making them fundamentally unexplainable and unpredictable. Furthermore, constructing AI systems that behave in ways that people expect is a significant challenge. If you fundamentally don’t understand something as unpredictable as AI, how can you trust it? (Image: Geralt via Pixabay Pixabay Licence)
India’s position on regulating AI has swung between extremes – from no regulation to regulation based on a “risk-based, no-harm” approach.
In April this year, the Indian government said it would not regulate AI to help create an enabling, pro-innovation environment which could possibly catapult India to global leadership in AI-related tech.
However, just two months later the Ministry of Electronics and Information Technology indicated India would regulate AI through the Digital India Act.
Taking a U-turn from the earlier position of no-regulation, minister Rajeev Chandrasekhar said: “Our approach towards AI regulation or indeed any regulation is that we will regulate it through the prism of user harm.”
In a labour intensive economy like India, the issue of job losses because of AI replacing people is relatively stark.
However, the minister claimed: “While AI is disruptive, there is minimal threat to jobs as of now. The current state of development of AI is task-oriented, it cannot reason or use logic. Most jobs need reasoning and logic which currently no AI is capable of performing. AI might be able to achieve this in the next few years, but not right now.”
Such an assessment seems only partially correct because there are many routine, somewhat low-skill “tasks” that AI can perform. Given the preponderance of low-skill jobs in India, their replacement by AI can have a significant and adverse impact on employment.
Drafts of the upcoming Digital Personal Data Protection Bill 2023 leaked in the media suggest that personal data of Indian citizens may be shielded from being used for training AI.
It seems this position was inspired by questions US regulators have posed to Open AI about how it scraped personal data without user consent. If this becomes law — though it is hard to see how this can be implemented because of the way training data is collected and used — the “deemed consent” that allows such scraping of data in the public interest will cease to exist.
The Indian government’s position has clearly evolved over time. In mid-2018, the government think tank, Niti Aayog, published a strategy document on AI. Its focus was on increasing India’s AI capabilities, reskilling workers given the prospect of AI replacing several types of jobs and evolving policies for accelerating the adoption of AI in the country.
The document underlined India’s limited capabilities in AI research. It therefore recommended incentives for core and applied research in AI through Centres of Research Excellence in AI and more application-focused, industry-led International Centre(s) for Transformational Artificial Intelligence.
It also proposed reskilling of workers because of the anticipated job losses to AI, the creation of jobs that could constitute the new service industry and recognising and standardising informal training institutions.
It advocated accelerating the adoption of AI by creating multi-stakeholder marketplaces. This would enable smaller firms to discover and deploy AI for their enterprises through the marketplace, thus overcoming information asymmetry tilted in favour of large companies that can capture, clean, standardise data and train AI models on their own.
It emphasised the need for compiling large, annotated, dynamic datasets, across domains — possibly with state assistance — which can then be readily used by industry to train specific AI.
In early 2021, the Niti Aayog published a paper outlining how AI should be used “responsibly”. This set out the context for AI regulation.
It divided the risks of “narrow AI” (task-focused rather than a general artificial intelligence) into two categories: direct “system” impacts and the more indirect “social” impacts arising out of the general deployment of AI such as malicious use and targeted advertisements, including political ones.
More recently, seven working groups were set up under the India AI program by the government and they were to submit their reports by mid-June 2023. However, these are not available just yet.
These groups have many mandates — creating a data-governance framework, setting up an India data management office, identifying regulatory issues for AI, evaluating methods for capacity building, skilling and promoting AI startups, guide moonshot (innovative) projects in AI and setting up of data labs. More centres of excellence in AI related areas are also envisaged.
Policy makers are excited about designing the India datasets programme – its form and whether public and private datasets could be included. The aim is to share these datasets exclusively with Indian researchers and startups. Given the large population and its diversity, Indian datasets are expected to be unique in terms of the high range of training they could provide for AI models.
The Ministry of Electronics and Information Technology has also set up four committees on AI which submitted their reports in the latter half of 2019. These reports were focused on platforms and data on AI, leveraging AI for identifying national missions in key sectors, mapping technological capabilities, key policy enablers required across sectors, skilling and reskilling, and cyber security, safety, legal and ethical issues.
India’s position on regulating AI is evolving. It might, therefore, be worthwhile for the government to assess how AI regulatory mechanisms unfold elsewhere before adopting a definitive AI regulatory law.
The EU AI Act, for example, is still in the making. It gives teeth to the idea of risk-based regulation. The riskier the AI technologies, the more strictly would they be regulated.
AI regulatory developments in the US also remain unclear. One major hurdle to AI regulation is that its evolution is so fast unanticipated issues keep arising. For instance, the earlier EU AI Bill drafts paid little attention to generative AI until ChatGPT burst on the scene.
It may be prudent for India to see how the regulatory ethos evolves in Europe and US rather than rush in with a “comprehensive” law that might become outdated quickly.
Adopting the “risk-based, no-harm” approach is the right one to follow.
However, the most fundamental AI development is happening elsewhere. Instead of being worried about stifling innovation it might be prudent to prioritise cataloguing the specific negative AI-fallouts that India might face.
They could then be addressed either through existing agencies or develop specific regulations aimed at ameliorating the harm in question.
(Originally published under Creative Commons by 360info™. Read the original article here)
